There are different approaches for organizing information content extracted from text, such as decision graphs or data tables, namely, a knowledge-based approach and a data-based approach. Using the knowledge-based approach, a person (known as a knowledge engineer) interviews an expert in a given field to obtain knowledge about the given field. The knowledge engineer and expert first determine the distinctions of the subject matter that are important for decision making in the field of the expert. These distinctions correspond to questions about the variables in the domain of interest, referred to as the hypotheses. For example, if a decision graph is to be used to predict the age of a customer based on the products that customer bought in a store, there would be a variable for “age” and a variable for all relevant products. The knowledge engineer and the expert next determine the structure of the decision graph and the corresponding parameter values that quantify the conditional probability distribution.
In the database approach, the knowledge engineer and the expert first determine the variables of the domain. Next, data is accumulated for those variables, and an algorithm is applied that creates one or more decision graphs from this data. The accumulated data comes from real world instances of the domain or hypothesis. That is, real world instances of decision making in a given field. For some decision-making applications, however, it can be difficult in practice to find sufficient applicable data to construct a viable decision network.
Conventional decision network techniques which combine knowledge-based and data-based approaches include neural networks, Rough Set Theory, and belief networks, such as a Bayesian Belief Network or a Dempster-Shafer Belief Network. Neural networks don't provide robust explanations. Rough sets differentiate between what is certain and what is possible. The Bayesian networks provide intuitive results, but are better suited to causal reasoning. A Dempster-Shafer belief network is an evidential reasoning approach that relies on the Dempster-Shafer Combination Rule, which differentiates ignorance and disbelief (sometimes described as “skeptical” processing), and performs conflict resolution.
Decision makers often find it difficult to mentally combine evidence since the human tendency is to postpone risky decisions when data is incomplete, jump to conclusions, or refuse to consider conflicting data. Those versed in classical (frequentist) statistics realize that in situations where evidence is sparse, the use of a decision network is desirable. Unfortunately, traditional means of generating a decision network are labor intensive, requiring many hours of labor from an analyst knowledgable about the desired application.